FFNN [1]} is the most basic neural network used for different applications, including computer vision, natural language processing, and speech recognition. In FFNN, data propagates only in one direction, passing through neural nodes and exiting through output nodes. FFNN comprises input and output layers; however, hidden layers may or may not exist. The weights are static in FFNN, and an activation function is used by inputs multiplied by weights to learn the value of parameter \(\theta \) . In addition, the design of FFNN requires considering several crucial factors, including an optimizer, cost function, and output units. The cost function is minimized using an optimizer or optimization algorithm such as Adam [2]}, stochastic gradient descent [3]}, and Adagrad [4]}, which adjusts the values of the weights and biases after each training cycle or epoch until the cost function approaches the global optimum. In comparison, output units are the part of the output layer to provide the required output or prediction, thus completing the task that the neural network must complete. FFNN in Fig. REF  shows input passed through one or more hidden layers in a uni-directional way, and weights are multiplied by inputs to minimize the error using an optimizer.
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